The Crucial Role of Predictive Models in Childhood Asthma care: Improving Outcomes Through Data-Driven Insights
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Background
Asthma is one of the most prominent chronic diseases in children and one of the most challenging ailments to diagnose in infants and preschoolers in the United States. Predictive models can be instrumental in offering a data-driven approach to improve early diagnosis, personalize treatment strategies, and disease progression. By utilizing nationalized data, this study focuses on building and comparing high-performing analytical predictive models based on the 28 associated risk factors and identifying the most contributing factors influencing childhood asthma.
Method
Data came from the BRFSS (2011-2020) Asthma Call Back Survey (ACBS). The cross-sectional study included 9813 participants with a response rate of 65% (current asthma status positive). Respondents were randomly divided into training and testing samples. The grid-search mechanism was implemented to compute the optimum values of the hyper-parameters of the analytical eXtreme Gradient Boosting (XGBoost) model. The fitted XGBoost model was compared with four competing ML models, including support vector machine (SVM), random forest, LASSO regression, and GBM. The performance of all the models was compared using accuracy, AUC, precision, and recall. Variable importance plot (VIP) was used to measure the percentage of contribution of the predictors to the response, and Shapley Additive exPlanations (SHAP) plot was used to understand how the predictors are related to the outcome. Chi-square test was used to measure the association between the predictors and the outcome.
Results
Asthma diagnosis was found to vary by age group, with the highest prevalence in kindergarten age (31.44%). Of the five predictive models, the XGBoost was found to be the best performing model with AUC: 0.95, followed by random forest (AUC: 0.9345), GBM (AUC: 0.9341), SVM (AUC 0.9304), and LASSO (AUC 0.88); however, the random forest model was found to have the highest sensitivity (0.9786), and hence preferred for initial screening of asthma. The top two contributing predictors were overnight hospitalization visits and time since the last asthma medication, accounting for 24.62% and 20.92%, respectively, to the asthma status, from the VIP.
Conclusion
The analytical methodology of the model development was found to be instrumental in the discovery of behavior health-risk knowledge and to visualize the significance of predictive modeling from a multidimensional behavioral health survey. These insights can be instrumental in predicting different types of chronic lung diseases affecting people of all ages and can be useful for clinicians to diagnose asthma at an early stage, allowing for early intervention and proactive management.
